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Selecting between batch and zone order picking strategies in a distribution center
Abstract
An order picking strategy in a distribution center (DC) defines the manner in which pickers navigate the picking area to pick items from storage locations. We focus on the problem of selecting between a batch picking and a zone picking strategy. For this problem, we propose a cost model to estimate the cost of each type of picking strategy. In our cost model we consider the effects of pick-rate, picker blocking, workload-imbalance, and the sorting system requirement. Through an example problem, we show how system throughput, order sizes, item distribution in orders, and wavelength affect the picking strategy selection decision.
... In the literature, most studies on warehouse order picking have focused only on picker routing or order batching. Articles that only consider order picking can be classified into certain [4][5][6][7][8][9][10][11][12][13][14][15][16][17] and uncertain cases [18,19]. Picker routing can be considered a traditional traveling-salesman problem (TSP). ...
... Storage systems, capacity, and strategies were introduced in [14,17]. Zone picking was used as the picking method in [8,15,16]. Petersen [21] evaluated the returning characteristic in routing policies to ensure that pickers enter an aisle to collect the required items, exit through the path they entered, and then, travel to subsequent aisles in succession. Regarding algorithm design, the genetic algorithm (GA) can be seen in [9,15]. ...
... Constraint (7) indicates the capacity of the picking vehicle, signifying that the batch weight after order consolidation cannot exceed the maximum vehicle load. Constraint (8) indicates that all variables are binary. ...
Because of time and cost constraints, item picking plays a major role in warehouse operations. Considering diversified orders and a constant warehouse design, deciding how to combine each batch and picker route effectively is a challenge in warehouse management. In this study, we focus on the evaluation of order-batching strategies for a single picker facing multiple orders with the objective of minimizing the total traveling distance. We propose two-stage simulated annealing and variable neighborhood search algorithms to solve the combined problem. The orders are first merged into batches, followed by determining the sequence in each batch. The computational analysis revealed that the best-fit-decreasing (BFD) batch ordering strategy in the two-stage algorithms, the variable neighborhood search algorithm, obtained superior solutions to those of the simulated annealing algorithm.
... The limited number of papers that do investigate workload balancing in order picking use different balancing measures (i.e. measures to evaluate and correct imbalances) or study the relationship between workload balance and other performance criteria such as throughput or travel time (Jane 2000;Jane and Laih 2005;Parikh and Meller 2008;. This study differs from other studies on workload balancing in order picking, by comparing different balancing measures based on their effectiveness for balancing a solution itself. ...
... item to zone assignment), batch formation and batch to picker assignment (i.e. job assignment) (Parikh and Meller 2008). Several papers tackle the problem of workload balancing in the above mentioned planning problems. ...
... Simulation results show that balance among the zones leads to improvements in make span, lead time and reduces congestion. Parikh and Meller (2008) develop a cost model to estimate the cost of zone and batch picking strategies. The effects of pick-rate, picker blocking, workload imbalance and sorting system requirements are evaluated. ...
An intensified competition forces warehouses to handle more orders in shorter time windows. This complicates the timely retrieval of these customer orders. Planning order picking operations, thereby aiming to increase efficiency, inevitably results in balancing concerns, such as imbalances among pick areas, pickers or time periods. Reducing workload imbalances, therefore anticipating on workload peaks, results in a more stable order picking process. However, there exist several measures that can be used to evaluate and correct existing imbalances. This study contributes to academic literature by analysing, explaining and evaluating the effectiveness of various workload balancing approaches (e.g. Rawlsian's approach, range, mean-based) in order picking operations, more specifically in the context of balancing workload over time in case of restricted time windows for retrieving customer orders. Results show that the effect of warehouse layout characteristics and customer order parameters on the effectiveness of balancing measures is very limited. However, the underlying managerial reason (e.g. workforce allocation, transportation schedule or human well-being) for solving the operational workload balancing problem does significantly impact the effectiveness and choice of an appropriate balancing measure.
... In the literature, researchers have used different metrics to measure warehouse outputs, including movement, accumulation, and service level [26][27][28]. Movement is the number of items handled by the pickers. Here, we assumed that all items picked are shipped in their respective orders. ...
... Now, using the picking rate and the batching model proposed by [27], we were able to generate the number of daily fulfilled orders and an estimation of the associated costs incurred by the warehouses. ...
... The usage hours are calculated using an eight-hour work shift for 220 days a year. We used CPLEX 12.10 (with default options and 60 s time limit for each run) to solve the MILP models Ref. [24,27] and calculate the cost efficiency values, Matlab R2020a to implement the method of [34], and Microsoft Office Professional Plus 2019 to compile the data generated. The software are run on an ASUSPRO with 3.00 GHz processor and 24 GB RAM with a Microsoft Windows 10 Enterprise operating system. ...
Featured Application
The framework presented in this study can be served as a decision tool to help warehouse managers choose the right picking technology.
Abstract
Recent literature demonstrates that warehouse order picking performance is reflected in the logistics performance of downstream retailers. Warehouse solutions and policies significantly contribute to the improvement of distribution and delivery to retailers. This paper therefore reports an analysis of the joint performance of routing policies and picking technologies, and provides insights into the best ways to combine routing strategies and paperless solutions in order to optimize cost efficiency. We follow a multistage approach that combines mixed integer linear programming algorithms, data envelopment analysis (DEA), and ranking and selection. The results show that traversal-voice picking and midpoint-voice picking combinations are equally distributed over the most efficient subsets and that superior technology can enhance picking efficiency only to a certain level. The study provides guidelines for logistics managers on ways to combine warehouse solutions and policies in order to better streamline the operations. It offers an original framework to analyze the joint performance of picking routing and picking solutions by considering the effect of picking errors.
... Among the studies that tackle how best to select a picking strategy, there are some that tend to evaluate the performance of different strategies by using a cost function. This function aggregates different criteria that affect the picking activity, and, the selection is normally made based on comparing operational costs (Parikh and Meller 2008). Aspects such as the picking workforce, equipment, order backlogs, the labor force of the classification activity, and the cost of the classification To the best of our knowledge the multicriteria approach has not yet been used to directly address how best to choose a picking strategy. ...
... C 2 : Rate of error. Average number of items misclassified within an order due to human mistakes (Parikh and Meller 2008;Grosse and Glock 2013). C 3 : Average response time. ...
... The data and variables used in the numerical experiment correspond to adaptations of the information found in previous studies on order-picking, specifically Parikh et al. (2008), Petersen (1999), Petersen (2009), and Shiau and Liao (2013). Table 3 shows the performance matrix, obtained as a result of the simulation experiment above and the description of the assessment criteria. ...
Choosing an order picking strategy is one of the most important decisions related to warehouse management. Making this decision properly can lead to high standards of efficiency, since order picking represents more than a half of a wholesale and retail organization’s operational costs and consumes a huge amount of the resources allocated to warehouse labor. Moreover, some productivity and service-oriented objectives related to order picking are sometimes conflicting, and require managers’ preferences to be considered, thus making the decision problem multi-objective and complex. We put forward a multicriteria decision model based on the ELECTRE III method that supports how to choose an order picking strategy. It takes managers’ preferences into consideration and integrates all the core elements for assessing how picking is being performed. Results showed that the model is able to identify the strategy that yields the best compromise between the objectives of productivity and the service-oriented ones, and that this strategy also represents the organization’s aims.
... This assignment can be based on product properties (e.g., size weight, safety and temperature requirements) or demand properties (e.g., customer type) (Jane, 2000). Zone picking defines the flow of customer orders through all pick zones, which can be in parallel or sequential (Parikh and Meller, 2008). ...
... As a consequence, concerns about workload equity have gained increasing attention (Vanheusden et al., 2020). Equitable workload allocations need to be considered when for example products are allocated to zones (Jane and Laih, 2005), customer orders are assigned to pick waves (Vanheusden et al., 2020) and batches are assigned to order pickers (Parikh and Meller, 2008;Fibrianto and Hong, 2019). Considering equitable workload allocations in warehousing may lead to several benefits: a better usage of bottleneck resources (e.g., pick trucks), increased worker moral and an increase in overall picking efficiency (Matl et al., 2017;Vanheusden et al., 2020). ...
... Picker blocking is a practical factor that causes idle time for order pickers and therefore also increases total throughput times of the warehouse (Parikh and Meller, 2008). Picker blocking often occurs in practice, and therefore it is of growing interest in academic research in the last ten years. ...
Market trends such as globalisation, increasing customer expectations, expensive industrial land and high labour costs cause a need for efficient order picking systems in practice. However, managers often do not implement findings from academic research on order picking planning into practice because researchers hardly account for practical factors (e.g., high-level storage, human factors, pick vehicle properties) or make unrealistic assumptions in their solution algorithms. A state-of-the-art review of the scientific literature on order picking planning (1) identifies and classifies highly influential practical factors, (2) shows the impact of these practical factors on order picking performance, and (3) illustrates how existing order picking planning models should be elaborated to account for practical factors. This study contributes to close the gap between research and practice by guiding future researchers to further increase the practical applicability of their research results.
... Among these systems, sequential zone-picking lines [7] have recently attracted considerable attention. Sequential zone-picking lines are beneficial because they are easy to implement, and the downstream sorter can be eliminated [8]. Nevertheless, the line fixes the path of the box; thus, the box must visit every zone in the system [9]. ...
... Its formulation is given by Eqs. (8) and (9), respectively. Note that the problem solution satisfying the Karush-Kuhn-Tucker condition is optimal because of the concavity of Eq. (8) [20]. ...
... Items are picked by batches, not by orders; thus, the picked items in a batch need an additional order sorting process to resort to the original orders (Boysen, Stephan, & Weidinger, 2019;Chabot, Lahyani, Coelho, & Renaud, 2017;De Koster, Der Poort, & Wolters, 1999). If the order sorting process and the order picking process is processed simultaneously, it is called the sort-while-pick strategy ( Fig. 1(a)) (Parikh & Meller, 2008). Fig. 1(a) shows that there are several boxes in the cart. ...
... Each time the picker picks an item, he places it in the box corresponding to the order it belongs to. If the order sorting process is placed downstream after the order picking process, it is called the pick-and-sort strategy ( Fig. 1(b)) (Parikh & Meller, 2008). Fig. 1(b) shows that there are no boxes in the cart. ...
Under the pick-and-sort order batch processing strategy, orders successively go through an order picking process, a buffer area, and an order sorting process in the form of batches. Owing to the difference in order batches and the limitation of the buffer capacity, blocking in the picking and starving in the sorting often occurs. This leads to a longer order completion time. Therefore, to minimise the total order completion time, this study investigates the order batching and sequencing problem under this strategy to reduce or even eliminate the blocking and starving phenomena. We formulate this problem as a mathematical model. By analysing the model, two useful properties and a lower bound are obtained. Based on these properties, we propose a situation-based seed (SBS) algorithm to solve the problem, which provides the intelligence of coordination for the classical seed algorithm. Numerical experiments demonstrate the superiority, effectiveness and optimality of the proposed algorithm. The proposed algorithm can save 8.359% of the total order completion time compared with the real-life algorithm applied in pick-and-sort warehouses, and its performance is close to the lower bound (only 1.105% gap). In addition, we also provide some management insights on how to coordinate the picking and sorting processes in practical pick-and-sort warehouses.
... The sorting function has not been modeled directly in these studies and their results in limited amount of knowledge about sorting function, since the relationship between sorting and picking has not been determined. Parikh and Meller [41] examined the suitability of a batch picking and zone picking for existing distribution center, and a cost model for choosing between picking strategies was proposed. An analytical approach was developed by Van Nieuwenhuse and de Koster [6] in order to estimate throughput time of the expected system in an on-line ordering system for both VTWB and FTWB. ...
... Moreover, in this research, operator routing within blocks is based on s-shaped routing. This paper extends the work of Tang and Chew [40], Le-Duc and de Koster [5], Parikh and Meller [41], and Yu and de Koster [17] into a multiple zones situation. Recently, the global supply chain management has paid special attention on small and frequent delivery of the orders with lower total costs due to advent of e-commerce and e-business[50]. ...
With increase in the inventory of stored items and in the number of orders received, the picking process and the response time gain greater importance. It should be noted that, in order to enhance the efficiency of warehouse management system, effective correlation and coordination between order batching and order picking process is of crucial role. In this paper, novel mixed integer nonlinear programming for on-line order batching is proposed for improving performance of the warehouse which in turn results in reducing the response time and idle times. The proposed method is based on a blocked warehouse using a zoning system, which is called Online Order Batching in Blocked Warehouse with One Picker for each Block (OOBBWOPB). The mentioned model is solved by using two algorithm of artificial bee colony (ABC) and Ant-colony (ACO). For proving the analyses and claims, two numerical examples as cases 1 and 2 are defined and analyzed by this algorithms in MATLAB environment. Based on the results, the proposed warehouse shows better performance with a substantial reduction in the average response time of a set of customer orders compare to zhang et al. (2017) results. It’s noteworthy that the ACO yields better results than ABC.
... In such cases, single order picking may cause pickers to walk the same routes or access the same storage locations repeatedly. Therefore, batch order picking is commonly used to improve the order picking efficiency in practice [14]. Obtaining high-quality batching results is key to enhancing the efficiency of batch picking. ...
With the rapid development of e-commerce, the scattered storage mode has been widely applied in B2C distribution centers in which there is a large assortment and quantity of small-sized, time-sensitive orders. Under the scattered storage mode, obtaining high-quality batching results and quickly completing order picking are key to improving the operation efficiency of a distribution center when a large number of orders arrive in a short period. Against this background, a new order batching problem under the scattered storage mode is studied. The feature is to improve the batching quality by considering the correlation between products. The problem is formulated as a 0–1 integer programming model to maximize the sum of pair-to-pair order correlations in all batches. To solve large-scale problems, we first propose two new seed batching algorithms based on the correlation between products. The first one selects the order with the largest number of products as the seed order, and the second one selects the order with the highest correlation as the seed order. Then tabu search (TS) is used to improve these two algorithms. In addition, a new seed batching algorithm for a special situation is proposed, which needs to use the location information of each product to obtain more accurate batching results. Finally, an improved two-stage order picking algorithm is proposed to verify the actual picking effect of the batching results obtained from the different algorithms. The experimental results show that the two seed batching algorithms improved by TS are superior to the existing batching algorithms in batch quality for the general situation, and the second seed batching algorithm improved by TS performs better for large-scale problems. Moreover, the new seed batching algorithm is more efficient and effective.
... The synchronized category involves simultaneous order preparation across multiple zones, and consolidation occurs in the final phase in the P/D. Significant contributions to the ZPP have been made by researchers such as Saylam et al. (2023), Ho and Lin (2017), and Parikh and Meller (2008). This research has expanded knowledge about zone selection strategies and provided valuable contributions to optimization techniques for the ZPP. ...
This paper presents optimized solutions for the Integrated Joint Zone Picking-Order Batching and Vehicle Routing Problem with Time Window (ZPOBVRPTW). The ZPOBVRPTW combines three distinct optimization problems: the Order Batching Problem (OBP), the Zone Picking Problem (ZPP), and Vehicle Routing Problem with Time Windows (VRPTW). The NP-hard nature of these problems and the need for enhanced customer service levels within and outside Warehouses (WAs) have created a lack of optimization methods that effectively integrate the ZPP, OBP, and VRPTW. This paper addresses practical challenges that have not been extensively explored in previous research on the ZPOBVRPTW. The main objective is to minimize delay costs and travel distance, optimizing customer reach and improving the overall efficiency of WAs. Therefore, small and medium instances are solved by developing a new Mixed Integer Linear Programming (MILP). For large instances, two metaheuristics were proposed which are a Genetic Algorithm (GA) and a Weighted Superposition Attraction (WSA) algorithm. The proposed solution methods for the ZPOBVRPTW were evaluated using experiments in four zoning scenarios of a single-block WA. The computational results showed that the algorithms successfully solved the ZPOBVRPTW. The optimal solutions were obtained in a relatively short time by GA, WAS, and CPLEX Solver. Nonetheless, for medium and large-scale datasets (instances), GA (27% faster) outperforms WSA. This research fills gaps in the literature and by advancing the management practices of WAs provides a solution that integrates the different problems that comprise the ZPOBVRPTW.
... The operational efficiency of sorting centers not only affects the speed of goods turnover but is also directly related to the control of logistics costs and the quality of customer service experience. Among them, staff scheduling, as an important part of the daily management of sorting centers, has a significant impact on improving work efficiency, reducing labor costs, and protecting the rights and interests of employees [2]. ...
With the rapid development of the logistics industry, the efficient operation of logistics sorting centers is crucial to the entire supply chain's efficiency. Staff scheduling, as a key aspect of the daily operations of sorting centers, directly impacts the control of labor costs and the job satisfaction of employees. To address the scheduling problem and enhance operational efficiency, this study proposes a personnel scheduling optimization model for logistics sorting centers based on linear programming and heuristic algorithms. Firstly, this paper analyzes the sorting center's cargo volume data and staffing requirements, clarifying the goal of scheduling optimization to meet workload demands while minimizing the total number of workdays, taking into account the different characteristics of regular and temporary workers, as well as legal regulations on working hours. Subsequently, a standard linear programming model is constructed, with the number of regular and temporary workers for each shift of every day as decision variables, to minimize the total workdays through linear optimization. However, due to the expansion of the problem scale, the solution time for the linear programming model has increased significantly. Therefore, a heuristic search algorithm is introduced to find an approximate optimal solution. Ultimately, through the analysis of actual cases, the effectiveness and practicality of the proposed model and algorithm are verified. This study's research not only provides a new method for personnel scheduling optimization in logistics sorting centers but also offers a reference for other industries requiring personnel scheduling optimization. Future research can further explore the applicability of the model in sorting centers of different scales.
... The operational efficiency of sorting centers not only affects the speed of goods turnover but is also directly related to the control of logistics costs and the quality of customer service experience. Among them, staff scheduling, as an important part of the daily management of sorting centers, has a significant impact on improving work efficiency, reducing labor costs, and protecting the rights and interests of employees [2]. ...
With the rapid development of the logistics industry, the efficient operation of logistics sorting centers is crucial to the entire supply chain's efficiency. Staff scheduling, as a key aspect of the daily operations of sorting centers, directly impacts the control of labor costs and the job satisfaction of employees. To address the scheduling problem and enhance operational efficiency, this study proposes a personnel scheduling optimization model for logistics sorting centers based on linear programming and heuristic algorithms. Firstly, this paper analyzes the sorting center's cargo volume data and staffing requirements, clarifying the goal of scheduling optimization to meet workload demands while minimizing the total number of workdays, taking into account the different characteristics of regular and temporary workers, as well as legal regulations on working hours. Subsequently, a standard linear programming model is constructed, with the number of regular and temporary workers for each shift of every day as decision variables, to minimize the total workdays through linear optimization. However, due to the expansion of the problem scale, the solution time for the linear programming model has increased significantly. Therefore, a heuristic search algorithm is introduced to find an approximate optimal solution. Ultimately, through the analysis of actual cases, the effectiveness and practicality of the proposed model and algorithm are verified. This study's research not only provides a new method for personnel scheduling optimization in logistics sorting centers but also offers a reference for other industries requiring personnel scheduling optimization. Future research can further explore the applicability of the model in sorting centers of different scales.
... Roodbergen et al. (2008) developed an optimisation model to minimise the distance travelled inside a warehouse with the goal of providing a suitable layout structure; as found, the layout that resulted from the model was similar to the layout that resulted from the simulation packages, but with a better travel distance by utilising the S-shape routing. Parikh and Meller (2008) studied the problem of selecting between the batch-picking strategy and the zone-picking strategy. The authors developed a cost-estimation model to compare between these two strategies from the cost viewpoint. ...
This article aims to investigate the impact of allowable human energy expenditure (HEE) of order pickers on the throughput of workers in manual order zone picking systems MOP. The method used in this research is the Monte Carlo simulation, used while considering many human and job factors. The results showed that a worker’s gender and an item’s weight have little effect on the HEE. On the other hand, body weight, walking speed, distance travelled, and the targeted zone significantly impacted the HEE, rest allowance, and throughput. For example, male pickers at a weight of 75 kg can move up to speed to 1 m/s and pick up items weighing up to 5 kg without reaching the allowable HEE rate, equal to 4.3 kcal/min, and, thus, no rest is needed. Female pickers at a weight of 75 kg reach the allowable HEE rate, equal to 2.6 kcal/min, at a very low speed of approximately 0.1 m/s when picking up items up to 5 kg, and, thus, frequent rest is needed, which leads to low throughput. To increase the throughput of female pickers, they can be assigned to pick up lighter items. Utilising Monte Carlo simulation to evaluate the HEE in MOP while considering many factors.
... In the literature, zone picking systems are sometimes analysed separately from the batch picking concept. For example, Parikh and Meller [33] treat zone picking and order-batching separately, and evaluate both ideas using the cost model. ...
Pick-and-pass systems are a part of picker-to-parts order-picking systems and constitute a very common storage solution in cases where customer orders are usually small and need to be completed very quickly. As workers pick items in the zones connected by conveyor, their work needs to be coordinated. The paper presents MILP models that optimize the order-picking process. The first model uses information about expected demand for items to solve the storage location problem and balance the workload across zones. The task of the next model is order-batching and sequencing – two concepts are presented that meet different assumptions. The results of the exemplary tasks solved with the use of the proposed MILP models show that the total picking time of a set of orders can be reduced by about 35-45% in comparison with random policies. The paper presents an equation for the lower bound of a makespan. Recommendations about the number of zones that guarantee the required system efficiency are also introduced.
... In addition, 9.68% of the articles has evaluated both aspects. Among those studies that aim at reducing the system's cost, Parikh and Meller (2008) have focused on the issue of selecting between a batch picking and a zone picking strategy. For this problem, the authors proposed a model to estimate the cost of both types of picking strategy. ...
A literature review on the order picking process in warehouses is presented for delineating the trends in time of research topics in this field. A total of 269 journal papers published between 2007 and 2022 were retrieved from Scopus. After a methodological classification, descriptive analyses were performed on authors, journals, subject area and top publishing countries. Bibliometric tools were used to map the topics covered by the reviewed studies, categorise them and determine possible relationships. Papers’ contents were evaluated in terms of eight categories, including five typical issues of order picking systems, plus three aspects dealing with the characteristics of the application. Insights about the extent to which these aspects have been covered in the literature are derived; relationships between the various aspects of the picking process are also delineated. Suggestions for future research activities are finally deducted, offering researchers and practitioners strong bases for works on order picking systems.
... Total picking is a method of picking up many customer orders at the same time. For each customer order, products must be sorted, usually using a pick-and-sort or sort-while-pick technique [40]. Single picking methods, on the other hand, are those in which only one customer order is picked at a time [45]. ...
Shelf space is one of the essential resources in logistic decisions. Order picking is the most time-consuming and labour-intensive of the distribution processes in distribution centres. Current research investigates the allocation of shelf space on a rack in a distribution centre and a retail store. The retail store, as well as the distribution centre, offers a large number of shelf storage locations. In this research, multi-orientated capping as a product of the rack allocation method is investigated. Capping allows additional product items to be placed on the rack. We show the linearisation technique with the help of which the models with capping could be linearised and, therefore, an optimal solution could be obtained. The computational experiments compare the quality of results obtained by non-linear and linear models. The proposed technique does not increase the complexity of the initial non-linear problem.
... While designing an OPS [11], a designer must consider the following question: which picking system best meets a given set of objectives? Some of the objectives a designer is required to optimize include maximizing throughput or minimizing cost, space, response time, or error-rate, or a combination thereof. ...
The benefits resulting of storage function still justify the substantial costs arising from them. Therefore, the storage system has two fundamental and conflicting objectives-reducing operation costs and increase service level. Solving this type of task can be set up as: an issue of design problem and control problem. This paper considers the problems of designing the warehouse, primarily on warehouses with the dominant order-picking function (a typical distribution warehouses). Reason is that this function generates significantly the cost of warehouse but also determines the service level for their customers. As a result, a design of such warehouses is primarily focused on resolving of order-picking technology selection. This problem, despite its importance, is not adequately present in the literature. This paper proposes an approach that could be used to support the selection of case picking technology in the design of the warehouse. 1 INTRODUCTION AND LITERATURE OVERVIEW Warehouses are an essential part of logistics processes and supply chains. There are a lot of reasons which support existence of warehouse and they are very well described in available literature [1]. In supply chains there are various types of warehouses, among them distribution warehouse is specific one. According to Berg [2], a distribution warehouse is a warehouse in which products from different suppliers are collected (and sometimes assembled) for delivery to a number of customers. The main function of distribution warehouse is to store products and fulfill external customer orders, typically composed of the large number of order lines (where each order line specifies a quantity of one particular product). The key objectives of such warehouses are to, be able quickly to fill orders with the minimum amount of effort and costs. That is the main task of order picking (OP) function and procedure of designing such warehouses should mostly focus to solving this function. From that reason, the choice of appropriate concept of order picking systems (OPS) has great impact on realization of this function and performances of entire warehouses. In many projects, order picking requires most of the operative personnel and offers the best opportunities for saving costs and improving performances. It is therefore advisable to plan the OPS first, and to develop the storage and transport systems afterwards. [3]. When selecting OPS, designer has to choose the applicable technological concepts provided a decision to introduce order pick area (OPA) by specifying the type of the space and the selection of technology inside that area (an equipment(s) type/number and suitable operational policies). Numerous design and cost parameters, combined with an endless variety of equipment types, make it difficult to specify an OPS. So, this problem, despite its importance, is not adequately present in the literature. Certain aspects (for example choice of type of OPA,
... A common configuration for the management of order picking activities in the warehouse consists in its subdivision in zones (we speak in this case of Zone Picking [5]). This strategy is particularly efficient to reduce the risk of congestion and improve picking performance, both in automated and manual warehouses [6]. ...
Manual order picking, the process of retrieving stock keeping units from their storage location to fulfil customer orders, is one of the most labour-intensive and costly activity in modern supply chains. To improve the outcome of order picking systems, automated and robotized components are increasingly introduced creating hybrid order picking systems where humans and machines jointly work together. This study focuses on the application of a hybrid picker-to-parts order picking system, in which human operators collaborate with Automated Mobile Robots (AMRs). In this paper a warehouse with a two-blocks layout is investigated. The main contributions are new mathematical models for the optimization of picking operations and synchronizations. Two alternative implementations for an AMR system are considered. In the first one handover locations, where pickers load AMRs are shared between pairs of opposite sub-aisles, while in the second they are not. It is shown that solving the mathematical models proposed by the meaning of black-box solvers provides a viable algorithmic optimization approach that can be used in practice to derive efficient operational plannings. The experimental study presented, based on a real warehouse and real orders, finally allows to evaluate and strategically compare the two alternative implementations considered for the AMR system.
... In the case of picking with multiple workers, a method called zoning is used to divide a logistics warehouse into several zones and assign workers to each area. In general, there are two methods: Pick-and-Pass and Parallel (De Koster et al., 2007) (Parikh and Meller, 2008). In Pick-and-Pass, workers work by item list; if the item in the list exists in each zone, the worker picks it and passes it to the worker in the neighboring zone. ...
In logistics warehouses, customer needs have diversified and product life cycles have shortened. Hence, it is necessary to deliver various types products to retailers in a short period of time while responding to fluctuating demand. It is important to increase the efficiency of the order picking in warehouses. When multiple workers pick simultaneously, psychological stress develops due to the presence of other workers. When other workers pick on the storage rack, it is not possible to work on the same storage rack, leading to psychological waiting stress until picking of other workers is completed. In order picking, it is important to consider not only the work time but also the stress of workers. However, no research considers the psychological stress in order picking. We used multi-agent simulation and proposed a storage assignment change plan under fluctuating demand considering work time and psychological stress. We determined the optimal pattern of storage assignment change that minimizes work time or psychological stress according to the fluctuating demand. Assignment of work areas based on the optimal pattern of storage assignment changes further reduced work time and stress. By determining the reduction of either work time or stress and selecting the pattern of storage assignment change and assignment of work areas, flexible and appropriate picking operations can be performed according to the daily situation.
... Basic picking strategies include discrete picking and batch picking (Van Gils et al., 2019). They are usually combined with other strategies like zone picking (Parikh and Meller, 2008) and wave picking (Van Gils et al., 2018). In addition, automated and robotized picking has also caught attention recently. ...
This paper treats intralogistics processing in a typical robotic forward-reserve e-commerce warehouse involving a reserve area with manual order-picking operations and a forward area with robotic parts-to-picker order sorting operations. It is critical to synchronize intralogistics operations between the two areas considering delivery requirements to optimize the performance in terms of makespan and costs. This challenge is formulated as a delivery-driven intralogistics synchronization (DDIS) problem. This paper develops a tailored variable neighborhood search solution method. A series of comprehensive numerical experiments under various scenarios show the superiority and stability of the DDIS as benchmarked with sequential approaches. A substantial reduction can be achieved in both makespan and forward area size, indicating significantly improved intralogistics operational efficiency and space utilization. Managerial insights are also discussed for specific action plans regarding the market size, the labor force, and the trolley configuration.
... Zoning divides the picking area into zones with one order picker being responsible for each zone . Examples include the works of Chia Jane (2000), Jane and Laih (2005), Parikh and Meller (2008), Yu and De Koster (2009), De Koster et al. (2012), and Pan et al. (2015. Storage assignment determines how items should be assigned to storage locations in the warehouse (Glock and Grosse, 2012;Glock et al., 2019). ...
Warehouse order picking is a critical operation for every supply chain because of its direct influence on customer satisfaction and the high time investment that is usually required for completing it. Hence, improving order picking efficiency can enhance both customer satisfaction and warehouse throughput. The most critical step in order picking is the travelling along the aisles of the warehouse, which accounts for a major share of the total warehouse operating cost. To minimize this cost for the special case of a warehouse with a leaf layout, the paper at hand proposes an efficient optimal order picker routing policy based on an Eulerian graph and a dynamic programming procedure. It further develops four simple routing heuristics, referred to as leaf S-shape, leaf return, leaf midpoint, and leaf largest gap, for guiding the order picker through the leaf warehouse. The performance of the routing heuristics is compared to the exact algorithm proposed in this paper in a systematic numerical experiment. Finally, we present some managerial insights for managing order picking in the leaf warehouse.
... Fortunately, these concerns could be fully tackled in the smart warehouse, since the manual labor is replaced by automated and autonomous devices. In the literature, several studies also consider the order splitting policy while dealing with the large amount of orders and the warehouse zoning (Cergibozan and Tasan, 2019;Parikh and Meller, 2008). In real e-commerce warehouse practice, the order splitting policy is also applied by some new retailing companies, i.e. ...
With the rapid development of e-commerce and the new retail, the same-day or even same-half day delivery service is provided to compete for market share. Recently, an increased number of e-commerce companies implement the unmanned smart warehouses to improve the logistics efficiency. In order to further reduce the demand response time, a novel picking strategy is designed to firstly split the orders, and then assign the partial orders to different pickers. After all the order segments have been collected, it is shipped to the customer. Due to the inherent complexity of the problem, a two stage optimization model is introduced. In the first stage, an order splitting and batching strategy based on spatial measure is proposed. And a MIP model is constructed to minimize the total picking distance, which is then solved via a column generation based algorithm. In the second stage, the newly formed batches are considered as a priori inputs, which are then assigned to automatic pickers. The picking process is modeled as a special parallel machine scheduling problem with multiple due dates for a single item, which could reduce to a customer order scheduling problem on parallel machines, and it is unary NP-complete. A heuristic method is proposed to obtain an approximate solution. Although the order splitting technique is not new in the logistics industry, the split orders are picked according to a vehicle routing problem, which fails to address the kitting issue for fast turnovers. In the numerical analysis section, the proposed algorithm is validated through extensive testing on various scales of instances. It is observed that the optimality gap for our algorithm is within 9%, and the computation time is around 5 min. Also, the average turnover rate increases by approximately 50% in comparison with the no-splitting policy. In most cases, the average order tardiness decreases by 90% compared with order splitting and no-kitting policy.
... Basic picking strategies include discrete picking and batch picking (Van Gils et al., 2019). They can also be combined with other strategies like zone picking (Parikh & Meller, 2008), and wave picking (Van Gils et al., 2018). Generally, picking strategies are quite flexible and should correspond to the actual requirements of warehouses. ...
Forward-reserve strategy is commonly applied when processing a large number of small orders. This paper considers robotic forward-reserve warehouses, where pick locations are brought to workers by mobile robots in the forward area. We treat the critical problem of unsatisfactory efficiency in the robotic forward area. A picking-replenishment synchronization mechanism (PRSM) is thereby proposed, which optimally attains a balance between replenishment efforts and picking efficiency. The resulting problem is formulated and solved by a tailored variable neighborhood search procedure integrated with a divide-and-conquer paradigm. Numerical results reveal remarkable benefits of PRSM over the conventional random schemes under different scenarios.
... The literature on zoning is rather limited. For more details, the reader should see [5,31]. In this paper, single and batch (sort-while-pick) OP methods are considered. ...
Order picking (OP) is a process that occurs in warehouses of unitized goods and it includes all activities that follow the retrieval of demanded goods, according to demanded type and quantity, with the intention of meeting customer demands, accurately and on time, with acceptable costs. As the OP process can contribute up to 50 % to the overall warehouse operational costs, design of the order picking system (OPS) needs special attention during the warehouse design process. In existing systems, the need for improvement is often caused by the change in demands. This paper analyzes the possibilities of low-level picker-to-part OPS improvement through appropriate decision making on different design levels: tactical and operational. In this paper, a simulation-analytical model is developed to evaluate different OPS scenarios based on three storage assignment policies, four routing strategies and two different OP methods. Two layout variants and the evaluation of the required workforce are considered as well. The experiment results indicate that the application of the proposed model could lead to significant improvements regarding OPS performances and resource utilization.
The organization of the product allocation on the shelf space of the warehouse rack with the creation of separate categories allows for a more rational use of available resources. This allows for optimization of warehouse space by ensuring a compact and logical product arrangement, making them easy to find and retrieve. This approach also reduces the amount of time spent on processing orders and reduces the likelihood of picking errors. As a result, the overall efficiency of warehouse operations and inventory management is improved. The purpose of the study is to develop a mathematical model of product allocation on a warehouse rack with simultaneous categorization of goods and shelves vertically and horizontally. The study is important for warehouse logistics.
The order-picking process has a significant impact on both the overall logistics costs as well as the customer service level, and as a result, is characterized as one of the highest priority warehouse activities for productivity and performance improvements. Although various existing studies focus on the design of order-picking systems as well as on a series of decision-making problems, there is still a need for a holistic framework that will map and categorize the parameters (e.g. total/forward area design, order-picking equipment, storage assignment policies, level of picking locations, order picking technologies, resource planning characteristics, order picking operational policies, routing strategies, product characteristics, order profile, etc.) that should be considered by researchers and practitioners for the design, control, and evaluation of order picking systems. To this end, the aim of this paper is to provide a state-of-the-art classification and review of parameters by adopting the Systematic Literature Review (SLR) approach. Overall, 389 articles were reviewed, and the identified parameters are classified into three categories: order-picking system design, order-picking system control, and order-picking system evaluation. Furthermore, this literature review aims to present managerial implications that directly affect the successful design of order-picking systems and identify a future research agenda.
The digitization and optimization of processes in the industry 4.0, especially in the context of assembly, is becoming more and more important. To improve the link between digital information and production technology is an essential step in the digitization effort, specifically for assembly. In this context, the connection of the real and virtual world is relevant for the system to be able to react to changes in the real world and vice versa. This paper presents and discusses three sequentially developed concepts for an assembly workstation. First, we developed a pick-by-light concept with paper guidance, and then we digitized the paper guidance and displayed it using an overhead projection on the work surface. Based on these concepts, we developed a tangible interface that allows the user to interact with the virtual system using physical objects. Applying an iterative development process, we were able to continuously improve our concepts. The three concepts were each evaluated individually and compared with each other. The results provide an insight into the improvement of the efficiency in processing times, picking times and picking errors.
The performance of two common picking strategies, batch picking (BP) and batch-synchronized zone picking (BSZP), are compared by considering pickers’ learning effects in online-to-offline (O2O) groceries. As we know from previous research, one major advantage of BSZP is that pickers can be more familiar with the item locations because each picker works in a relatively smaller picking area. However, few papers have formulated a quantitative method to verify this advantage. To address this, we use pickers’ learning effects to quantify their familiarity with the item locations, and then compare the picking efficiency in BP and BSZP with learning effects. The optimization models of BP and BSZP are formulated to minimize the total service time, and heuristic methods are proposed to solve these models. The case study is implemented under three order types and three storage assignment policies, i.e. Random, ABC-1, and ABC-2. The results of the study indicate that with learning effects, although BSZP needs a longer travel time than BP, it requires a shorter search time. Which strategy, BP or BSZP, is more efficient mainly depends on the learning rates, storage assignment policies, and pickers’ working days. When the storage assignments are Random and ABC-1, a smaller learning rate can lead BSZP to perform better than BP. When the storage assignment is ABC-2, BSZP performs better than BP. When pickers’ working days are long enough, the learning effect is weakened, and BP outperforms. Awareness of the learning effects is required to choose a better picking strategy, evaluate more accurate picking efficiency and hire an appropriate number of pickers.
Internal zone layout and picking are extremely important in the operation of a distribution warehouse. It is the most time-consuming and costly task in a distribution warehouse. Therefore, it is very important to study the efficiency and optimization of picking in the warehouse. In this paper, we propose an asynchronous parallel setting method for shelf zone layouts in logistics warehouses and build a simulation to show the efficiency improvement effect of the proposed method on real data.KeywordsTotal pickingMethod
ParallelSimulation
Market trends such as globalisation, increasing customer expectations, expensive industrial land and high labour costs cause a need for efficient order picking systems in practice. However, managers often do not implement findings from academic research on order picking planning into practice because researchers hardly account for practical factors (e.g. high-level storage, human factors, pick vehicle properties) or make unrealistic assumptions in their solution algorithms. A state-of-the-art review of the scientific literature on order picking planning (1) identifies and classifies highly influential practical factors, (2) shows the impact of these practical factors on order picking performance, and (3) illustrates how existing order picking planning models should be elaborated to account for practical factors. This study contributes to close the gap between research and practice by guiding future researchers to further increase the practical applicability of their research results.
This paper develops a novel strategic decision support framework for the design of an order picking system, which can be used whenever different systems and/or control mechanisms need to be compared for a given customer order structure. Warehousing companies frequently struggle in selecting the most suitable design for their order picking system. Traditionally, a comparison of different order picking systems is based on time-consuming simulation runs. In addition, the only source of consultancy is most often carried out by the order picking system manufacturers themselves. Our framework, using recent advancements in deep neural networks, provides an efficient methodology for selecting not only the best order picking system for a given order structure but also the most suited design parameters. This enables warehouse companies to compare objectively an extensive number of systems and allows them to identify the most promising order picking systems. We demonstrate our framework for a comprehensive comparison of three different fixed-path order picking systems to find one best suited for a provided order structure.
The Front Warehouse Mode (FWM) is currently popular in China where customers can order ready-to-use goods to be delivered to their homes in a short time. However, it is challenging to achieve timely order fulfillment economically due to the fragmented requirements. To handle the challenge, an integrated scheduling approach to link order picking and delivery closely is required. In this study, we focus on the integrated problem of zone picking and vehicle routing problem with time windows (ISZPVRPTW). Some real-life factors are considered to better plan the integrated problem, e.g. fulfillment deadlines, convey time between the picking zones, and variable delivery speeds. To help retailers optimally fulfill customers’ orders, we first present a mixed integer programming formulation and then develop a Two-stage Iterated Search (TIS) algorithm to minimize operation costs and overdue penalty costs. With extensive numerical experiments, we demonstrate the effectiveness and efficiency of algorithm TIS by comparing it with other approaches, e.g. the Traditional Sequential Scheduling (TSS), H-2 algorithm, and Gurobi. The results show that the TIS outperforms the TSS in different evaluation indicators and shows good performance. Moreover, the time allocation and caution intensity are significant for the fulfillment expense.
Order picking (OP) is a time and cost consuming operation in many warehouses. The optimization of order picking planning is crucial to speed up customers’ delivery process and reduce warehouse expenses. A commonly adopted strategy is called mixed-shelves storage strategy (MSSS) that is highly recommended for e-commerce warehouses. Applying this, the units of the same stock keeping unit (SKU) are scattered to various storage locations which provides more improvement room for OP. However, consolidated methods have not been sufficiently discussed when OP is under the MSSS. In this paper, we study the problem of wave picking systems to jointly address order batching, batch assignment, and picker routing (BAR) in a MSSS-based warehouse. We propose the decomposition of OP into BAR subproblems (DOPBAR) for solving such a wave OP problem. We examine our approach on a large set of instances, validate the effectiveness of our method, and point out the advantages of the MSSS in OP. Moreover, we analyze the trade-offs between two conflicting objectives that practitioners aim to optimize: customer service level (makespan) and workforce level (labor cost). Finally, a few insightful managerial recommendations are proposed that support the decision making in choosing a storage location assignment strategy.
In this paper, we study an automated warehousing system, where racks are moved by robots to multiple workstations so that pickers at each workstation can retrieve the products from the racks to fill up the orders. In this context, the order and rack sequences should be considered simultaneously and the workload balance and rack conflicts among multiple workstations should also be taken into considerations. However, these factors have not been addressed in the current literature. To fill this gap, we formulate a comprehensive multi-workstation order and rack sequencing problem as a mixed integer programming model that accounts for workload balancing and rack conflicts. To solve the model, we propose an adaptive large neighborhood search method, which builds on a newly developed data-driven heuristic that exploits the structure of the problem and simulated annealing. We show that our proposed approach performs well on both small-scale problem instances with synthetic data and a large-scale real-world dataset supplied by a large e-commerce company. In the latter case, it can save up to 62% in rack movements compared to the company’s current practice.
In this article, we describe the use of dynamic simulation when designing an effective system for order picking within a distribution warehouse. The simulation model was created in the Witness software environment for discrete dynamic simulation and is a modification of a general simulation model of material flows in supplier systems. Using the example of a batch system for picking orders in a drugstore goods warehouse, we discuss the possibilities of using a general simulation model of material flows as an effective framework for the development of system support for warehouse processes using WMS. The simulation model is based on the possibility of dividing any material flow in the supply system into a finite number of movements with the possibility of using one of the sources and fulfilment of certain conditions. In order to achieve the required optimisation of the order picking system, which depends, in particular, on the unknown duration of goods collection at the picking location, and on the duration of goods sorting in consolidation, the "what-if" analysis has been used as a tool to measure the impact of uncertainty of one or more variables entering the model on the uncertainty of output variables. The study showed that minimisation of the number of physical elements in the model leads to a significantly higher speed of its operation. By means of dynamic simulation, it is possible to test a large number of variants of the picking system layout in a relatively short time and minimise the risk of erroneous decisions associated with the implementation of a suitable WMS.
Purpose
This paper aims to examine the relationship between the main decisions for designing distribution centers (DCs) and the contextual characteristics of the distribution networks.
Design/methodology/approach
Experts were surveyed and responses analyzed quantitatively through multivariate data techniques. This study considered four contextual characteristics that were deemed as influential for DC design: types of routes in the distribution network, quantity of DCs, distribution network levels and company size.
Findings
This paper evidenced which decisions are affected by each contextual characteristic encompassed in this study. This paper identified that the characteristic types of route in the distribution network must be carefully considered, as it had the greatest amount of associations with the decisions for designing a DC.
Originality/value
Despite its importance, most studies on design of DCs disregard the effect of the context in which DCs are inserted. This research provides arguments to support decision-making process of DCs design, increasing assertiveness of their planning. This work fulfills a literature gap by empirically examining the effect of contextual variables on the decisions related to DC design. Regarding practice, this paper addressed a fundamental issue for managers looking to design a DC, as it evidenced how contextual characteristics impact the decision-making.
The paper presents the model for optimal number of merged orders that will reduce the average order-picking time. Two order batching policies were described: pick-then-sort and sort-while-pick. One-block rectangular warehouses were considered. The author studied two routing heuristics: s-shape and return for which the equations for average order-picking time can be designated based on probability distributions: binomial distribution and uniform distribution. The research takes into account the possible congestion problem. Based on simulations the analytical form of blocking time for pickers was proposed. The study shows that the advantages of each additional merged order is getting smaller. Using ABC classification for storage location assignment in warehouses with low number of aisles and large pick lists can extend the average order-picking time.
This research has aimed itself at capitalizing the benefits of CPS-enabled visibility and traceability technology for adaptive synchronization of pick-and-sort ecommerce order fulfilment. With assistance of IoT systems, the involved men, materials, and machines are turned into smart objects, deploying in the real-time environment. According to the real-time data collected from working stages, an adaptive synchronization mechanism with associated HGA-VNS algorithm is proposed to balance the picking simultaneity and sorting punctuality. Picking simultaneity attempts to make the items of a pick wave to be picked by several pickers in their particular working zone simultaneously and finished in a same time window, so that the subsequent sorting process could be started as early as possible. Sortation punctuality aims to ensure that the due date of an individual order is satisfied. The presented method is verified and evaluated via a case study. Trade-off analysis and sensitivity analysis are also examined with several key findings, considering varied order fulfilment scenarios such as slack / busy season, zoning effects, and item assignment policy.
This paper describes an approach to determine a layout for the order picking area in warehouses, so that the average travel distance for the order pickers is minimized. We give analytical formulas that can be used to calculate the average length of an order picking route under two different routing policies. The optimal layout can be determined by using these formulas as the objective function in a nonlinear programming model. The optimal number of aisles in an order picking area appears to depend strongly on the required storage space and the pick list size.
This work addresses the problem of order-picking in a rectangular warehouse that contains crossovers only at the ends of aisles. An algorithm is presented for picking an order in minimum time. The computational effort required is linear in the number of aisles. The procedure has been implemented on a microcomputer. A 50-aisle problem requires only about 1 minute to solve.
Considering a relay order pick system in a distribution center, proposes a heuristic method based on historical customers’ orders for assigning products into storage zones, and constructs a performance index for measuring the continuity of each order handled in the pick system. The objective is to balance workloads among all pickers so each one has almost the same load and the relay pick lane has a continuous flow. The proposed method is illustrated and verified to achieve the objective through empirical data and simulation experiments. Considering the fluctuation in order volume, presents two heuristic methods, also based on historical data, for adjusting the storage location so that the balanced workloads among all pickers and the continuity of the operation lane are not changed. These two methods are illustrated through actual data and verified by simulation experiments.
We present a literature survey on methods and techniques for the planning and control of warehousing systems. Planning refers to management decisions that affect the intermediate term (one or multiple months), such as inventory management and storage location assignment. Control refers to the operational decisions that affect the short term (hours, day), such as routing, sequencing, scheduling and order-batching. Prior to the literature survey, we give an introduction into warehousing systems and a classification of warehouse management problems.
This paper is concerned with the problem of order picking in mail order companies. Order picking is the retrieval of items from their warehouse storage locations to satisfy customer orders. Five order picking policies, strict order, batch, sequential zone, batch zone, and wave, are evaluated using labor requirements, processing time, and customer service as performance measures. A simulation model was developed to investigate these picking policies in a mail order environment. Prior research has focused on the study of individual picking policies. This study extends the prior research by evaluating multiple picking policies under varying operating conditions. The results of the study seem to indicate that (1) wave picking and batch picking perform well across the range of operating conditions considered in this study, and (2) sequential zone and batch zone picking do not perform well, especially as the order volume increases. However, the benefits and drawbacks to each picking policy must be taken into account. The key to effective implementation of an order picking system is to match the firm's business strategy, capabilities, technology, and space requirements with an order picking policy that maximizes the benefits of order picking to the firm and its customers.
We present a structured procedure for order pick system (OPS) analysis and design that has been established on literature review and interviews with and presentations to OPS experts. In particular, we attempt to include the thinking processes that occur between OPS designers and owners. The design procedure and related issues are discussed in the order of input, selection, and evaluation stages.
The cost and service performance of an order fulfillment center are determined partly by how workers are organized into an order picking system. One common approach is batch picking, in which workers circumnavigate a picking area with other workers, gathering items on a pick list. In some systems with high space utilization, narrow aisles prohibit workers from passing one another when in the same aisle, and this leads to congestion. We build analytical and simulation models of these systems to investigate their behavior under different levels of activity. Among other things, our results suggest that when the system is busier and pick density is high (that is, when workers stop often to make picks) congestion is less of a problem and workers are more productive.
The e-commerce revolution has raised awareness about the need for more efficient order fulfillment systems. Our research provides a design aid for companies engaged in order fulfillment system design, emphasizing the decision of whether or not to automate the sorting process. We have developed a descriptive model for this problem based on demand levels, labor rates, order sizes, and other factors. This model is incorporated into a simple cost-based optimization model to recommend a solution. To demonstrate the performance of the recommended system with stochastic factors, we have simulated the output of our model for several test problems. In the event that the model recommends a manual sorting system, we have provided an analytical model to aid in the design and operation of such systems by determining the optimal batching level.
Presents a methodology useful when analysing the efficiency of order
picking systems. The main feature of the analysis is the ability to
compare different system designs. The methodology has earlier been
applied mainly to assembly production systems, and has in these cases
proved to be an effective management tool in discussions concerning the
choice of production system.
This paper evaluates and compares strategies for routing a manual picker through a simple warehouse. It expands on previous work, in which optimization algorithms were developed, by deriving equations which relate route length to warehouse attributes. Several rules of thumb are derived for selection of order picking strategies and optimization of warehouse shape.
In designing an Order Picking System (OPS) with multiple pickers, the designing (or selection) of several parameters (e.g., width of aisles, storage system and picking strategy) is dependent on the blocking that occurs between pickers. In this paper analytical models to estimate blocking in an OPS that has picking aisles wide enough to allow pickers to pass other pickers in the aisle are developed. In such OPSs, pickers can experience blocking at a pick face when two or more pickers need to pick at the same pick face. The developed models are compared with simulation, with results indicating that the proposed models are sufficiently accurate. Test results suggest that when pickers pick one SKU at a pick face, blocking is less in a wide-aisle OPS compared to that in a narrow-aisle OPS. However, when pickers pick more than one SKU at a pick face, blocking increases monotonically with an increase in the number of SKUs picked. The last result is significant since it highlights the importance of the proposed model that considers the variation in the time the picker is stopped to pick.[Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resources: (i) Appendix A, which describes the procedure to obtain a closed-form expression for b1(2); (ii) Appendix B, which describes the derivations of the distributions for the case when pickers pick one SKU and pick:walk time ratio is ∞:1; and (iii) Appendix C, which describes the derivations of the distributions for the case when pickers may pick more than one SKU and pick:walk time ratio is ∞:1.]
This paper is concerned with the problem of order picking in mail order companies. Order picking is the retrieval of items from their warehouse storage locations to satisfy customer orders. Five order picking policies, strict order, batch, sequential zone, batch zone, and wave, are evaluated using labor requirements, processing time, and customer service as performance measures. A simulation model was developed to investigate these picking policies in a mail order environment. Prior research has focused on the study of individual picking policies. This study extends the prior research by evaluating multiple picking policies under varying operating conditions. The results of the study seem to indicate that (1) wave picking and batch picking perform well across the range of operating conditions considered in this study, and (2) sequential zone and batch zone picking do not perform well, especially as the order volume increases. However, the benefits and drawbacks to each picking policy must be taken into account. The key to effective implementation of an order picking system is to match the firm's business strategy, capabilities, technology, and space requirements with an order picking policy that maximizes the benefits of order picking to the firm and its customers.
"Bucket brigades" are a way of sharing work on a flow line that results in the spontaneous emergence of balance and consequent high throughput. All this happens without a work-content model or traditional assembly line balancing technology. Here we show that bucket brigades can be effective even in the presence of variability in the work content. In addition, we report confirmation at the national distribution center of a major chain retailer, which experienced a 34% increase in productivity after the workers began picking orders by bucket brigade.
Split-case sorting systems are used in many retail supply chains where items must be distributed in less-than-case quantities, such as orders shipped directly to customers by catalogers and e-tailers or shipments made in less-than-case quantities from a distribution center to a retail store. These systems are particularly effective in order-packing systems where the same item is needed for multiple orders. Items are inducted into a circular sorting conveyor system one unit at a time and then delivered to an order-packing bin designated for a particular customer or retail store. We develop analytical performance models that incorporate the stochastic operating conditions faced by these systems. Our model allows system designers to predict the sorting capacity for different system configurations. More importantly, we use the model to develop insight into the system design and operation.
In this paper we present a reference framework and a classification of warehouse design and control problems. Based on this framework, we review the existing literature on warehousing systems and indicate important gaps. In particular, we emphasize the need for design oriented studies, as opposed to the strong analysis oriented research on isolated subproblems that seems to be dominant in the current literature.
An extensive review on warehouse operation planning problems is presented. The problems are classified according to the basic warehouse functions, i.e., receiving, storage, order picking, and shipping. The literature in each category is summarized with an emphasis on the characteristics of various decision support models and solution algorithms. The purpose is to provide a bridge between academic researchers and warehouse practitioners, explaining what planning models and methods are currently available for warehouse operations, and what are the future research opportunities.
A warehouse is a service facility, often comprising the only view that customers actually have of a manufacturing firm. The management of this facility has significant leverage over order leadtimes and fill-rate reliability. As with other service facilities, system design and operation are decision problems that are closely interlinked. In this paper we describe and model in general terms the composite design and operating problems for a typical order-consolidation warehouse. These problems include warehouse layout, equipment and technology selection, item location, zoning, picker routing, pick list generation and order batching. The complexity of the overall problem mandates developing a new multi-stage hierarchical decision approach. Our hierarchical approach utilizes a sequence of coordinated mathematical models to evaluate the major economic tradeoffs and to prune the decision space to a few superior alternatives. Detailed simulation employing actual warehousing data is then used for validation and fine tuning of the resulting design and operating policies. We describe the application of this analytical approach to an automotive spare-parts distribution centre. The case study demonstrates substantial savings in operating costs and highlights several generic management tradeoffs.
In a synchronized zone order picking system, all the zones process the same order simultaneously. There may be some idle time when the zone pickers wait until all the pickers complete the current order. This paper develops a heuristic algorithm to balance the workload among all pickers so that the utilization of the order picking system is improved and to reduce the time needed for fulfilling each requested order. A similarity measurement, using customer orders, of any two items is first presented for measuring the co-appearance of both items in the same order. With this similarity measurement, a natural cluster model, which is a relaxation of the well-studied NP-hard homogeneous cluster model, is constructed. The heuristic algorithm is then proposed to solve the model for locating all the items into distinct zones. Finally, empirical data and simulation experiments verify that the objectives of the item cluster model are achieved.
An actual sorting system cost function for a sorter required to sort 1000 small-sized orders in a 40-min wave in a simultaneous zone picking system Performance of bucket brigades when work is stochastic
- Fig
Fig. 5. An actual sorting system cost function for a sorter required to sort 1000 small-sized orders in a 40-min wave in a simultaneous zone picking system. References Bartholdi, J.J., Eisenstein, D.D., Foley, R.D., 2001. Performance of bucket brigades when work is stochastic. Operations Research 49 (5), 710–719.
Research on warehouse operation: a comprehensive review Working paper, School of Industrial and Systems Engineering The effects of pick density on order picking areas with narrow aisles
- J Gu
- M Goetschalckx
- L F Mcginnis
- Ga Atlanta
- K R Gue
- R D Meller
- J D Skufca
Gu, J., Goetschalckx, M., McGinnis, L.F., 2005. Research on warehouse operation: a comprehensive review. Working paper, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA. Gue, K.R., Meller, R.D., Skufca, J.D., 2006. The effects of pick density on order picking areas with narrow aisles. IIE Transactions 38 (10), 859–868.
Facilities Planning A literature survey on planning and control of warehousing systems
- J A Tompkins
- J A White
- Y A Bozer
- J M A Tanchoco
Tompkins, J.A., White, J.A., Bozer, Y.A., Tanchoco, J.M.A., 2003. Facilities Planning, 3rd ed. John Wiley & Sons, New York, NY. van den Berg, J.P., 1999. A literature survey on planning and control of warehousing systems. IIE Transactions 31 (8), 751–762.
Configuring the storage system for order picking in a distribution center
- P J Parikh
- R D Meller
Parikh, P. J. and Meller, R. D. (2006a). Configuring the storage system for order picking
in a distribution center. Working paper, Grado Department of Industrial and Systems
Engineering, Virginia Tech, Blacksburg, VA.
Research on warehouse operation: a comprehensive review. Working paper, School of Industrial and Systems Engineering
- J Gu
- M Goetschalckx
- L F Mcginnis
Gu, J., Goetschalckx, M., and McGinnis, L. F. (2005). Research on warehouse operation:
A comprehensive review. Working paper, School of Industrial and Systems Engineering,
Georgia Institute of Technology, Atlanta, GA.